Dataset Information

Automated identification of sequence-tailored Cas9 proteins using massive metagenomic data.

ABSTRACT: The identification of the protospacer adjacent motif (PAM) sequences of Cas9 nucleases is crucial for their exploitation in genome editing. Here we develop a computational pipeline that was used to interrogate a massively expanded dataset of metagenome and virome assemblies for accurate and comprehensive PAM predictions. This procedure allows the identification and isolation of sequence-tailored Cas9 nucleases by using the target sequence as bait. As proof of concept, starting from the disease-causing mutation P23H in the RHO gene, we find, isolate and experimentally validate a Cas9 which uses the mutated sequence as PAM. Our PAM prediction pipeline will be instrumental to generate a Cas9 nuclease repertoire responding to any PAM requirement.

SUBMITTER: Ciciani M

PROVIDER: S-EPMC9617884 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Automated identification of sequence-tailored Cas9 proteins using massive metagenomic data.

Ciciani Matteo M Demozzi Michele M Pedrazzoli Eleonora E Visentin Elisabetta E Pezzè Laura L Signorini Lorenzo Federico LF Blanco-Miguez Aitor A Zolfo Moreno M Asnicar Francesco F Casini Antonio A Cereseto Anna A Segata Nicola N

Nature communications 20221029 1

The identification of the protospacer adjacent motif (PAM) sequences of Cas9 nucleases is crucial for their exploitation in genome editing. Here we develop a computational pipeline that was used to interrogate a massively expanded dataset of metagenome and virome assemblies for accurate and comprehensive PAM predictions. This procedure allows the identification and isolation of sequence-tailored Cas9 nucleases by using the target sequence as bait. As proof of concept, starting from the disease-c ...[more]

PMID: 36309502

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Project description:BackgroundMetagenomics is the application of modern genomic techniques to investigate the members of a microbial community directly in their natural environments and is widely used in many studies to survey the communities of microbial organisms that live in diverse ecosystems. In order to understand the metagenomic profile of one of the densest interaction spaces for millions of people, the public transit system, the MetaSUB international Consortium has collected and sequenced metagenomes from subways of different cities across the world. In collaboration with CAMDA, MetaSUB has made the metagenomic samples from these cities available for an open challenge of data analysis including, but not limited in scope to, the identification of unknown samples.ResultsTo distinguish the metagenomic profiling among different cities and also predict unknown samples precisely based on the profiling, two different approaches are proposed using machine learning techniques; one is a read-based taxonomy profiling of each sample and prediction method, and the other is a reduced representation assembly-based method. Among various machine learning techniques tested, the random forest technique showed promising results as a suitable classifier for both approaches. Random forest models developed from read-based taxonomic profiling could achieve an accuracy of 91% with 95% confidence interval between 80 and 93%. The assembly-based random forest model prediction also reached 90% accuracy. However, both models achieved roughly the same accuracy on the testing test, whereby they both failed to predict the most abundant label.ConclusionOur results suggest that both read-based and assembly-based approaches are powerful tools for the analysis of metagenomics data. Moreover, our results suggest that reduced representation assembly-based methods are able to simultaneous provide high-accuracy prediction on available data. Overall, we show that metagenomic samples can be traced back to their location with careful generation of features from the composition of microbes and utilizing existing machine learning algorithms. Proposed approaches show high accuracy of prediction, but require careful inspection before making any decisions due to sample noise or complexity.ReviewersThis article was reviewed by Eugene V. Koonin, Jing Zhou and Serghei Mangul.

Dataset Information

Automated identification of sequence-tailored Cas9 proteins using massive metagenomic data.

Publications

Automated identification of sequence-tailored Cas9 proteins using massive metagenomic data.

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